While Named Entity Recognition (NER) is a widely studied task, making
inferences of entities with only a few labeled data has been challenging,
especially for entities with nested structures. Unlike flat entities, entities
and their nested entities are more likely to have similar semantic feature
representations, drastically increasing difficulties in classifying different
entity categories in the few-shot setting. Although prior work has briefly
discussed nested structures in the context of few-shot learning, to our best
knowledge, this paper is the first one specifically dedicated to studying the
few-shot nested NER task. Leveraging contextual dependency to distinguish
nested entities, we propose a Biaffine-based Contrastive Learning (BCL)
framework. We first design a Biaffine span representation module for learning
the contextual span dependency representation for each entity span rather than
only learning its semantic representation. We then merge these two
representations by the residual connection to distinguish nested entities.
Finally, we build a contrastive learning framework to adjust the representation
distribution for larger margin boundaries and more generalized domain transfer
learning ability. We conducted experimental studies on three English, German,
and Russian nested NER datasets. The results show that the BCL outperformed
three baseline models on the 1-shot and 5-shot tasks in terms of F1 score.

本文首次提出了针对 few-shot 嵌套 NER 任务的 Biaffine 对比学习框架 (BCL)，该框架利用上下文依赖来区分嵌套实体，将语义表示与上下文跨度表示相结合，并采用对比学习来调整表示分布，实现更大的边缘边界和更广泛的领域迁移学习能力。实验结果表明，在三个英语、德语、俄语嵌套 NER 数据集上，BCL 在 1-shot 和 5-shot 任务中的 F1 得分优于三个基本模型。